Neural networks are currently the foundation for many modern artificial intelligence (AI) applications. The superior performance of neural networks comes from their ability to extract high-level features from raw sensory data after using statistical learning over a large amount of data to obtain an effective representation of an input space. Implementations of neural networks find applications in a myriad of applications from self-driving cars, to detecting cancer, and to playing complex games.
One specific application of neural networks for gesture and action recognition is sign language translation. A sign language (also known as signed language) is a language that uses manual communication to convey meaning, ideas and thoughts, which simultaneously employs hand gestures, audible cues (e.g., uttered sounds or clicks), movement, orientation of the fingers, arms or body, and facial expressions to convey a speaker's ideas. The complexity of sign language may be captured, in part, by using neural networks for its translation and communication.